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Current Tools

A list of ML & Data tools which are currently available in deployKF.


Tool Index

The following is an index of currently supported tools, grouped by ecosystem.

Tool Versions

See the version matrix to learn which versions of each tool are supported by each version of deployKF.

What tools are planned for future versions?

See the future tools page for a list of tools which are planned for future releases.

Kubeflow Ecosystem

Kubeflow is an "MLOps on Kubernetes" ecosystem which is owned by the CNCF, and provides various tools for building and deploying ML applications on Kubernetes.

Name
(Click for Details)
Purpose Since deployKF
Kubeflow Pipelines Workflow Orchestration 0.1.0
Kubeflow Notebooks Hosting Developer Environments 0.1.0
Katib Automated Machine Learning 0.1.0
Kubeflow Training Operator Managing Training Jobs 0.1.0
Kubeflow Volumes Managing Kubernetes Volumes 0.1.0
Kubeflow TensorBoards Managing TensorBoards 0.1.0

deployKF Ecosystem

Coming soon... See future tools for more information.

Tool Details

The following sections provide details and descriptions for each tool.

Kubeflow Pipelines

Kubeflow Pipelines (KFP) is a platform for building and running machine learning workflows on Kubernetes.

PurposeWorkflow Orchestration
MaintainerKubeflow Project
DocumentationDocumentation
Source Codekubeflow/pipelines
deployKF Configskubeflow_tools.pipelines
Since deployKF0.1.0

KFP provides higher-level abstractions for Argo Workflows to reduce repetition when defining machine learning tasks. KFP has abstractions for defining pipelines and reusable components which it can compile and execute as Argo Workflows.

The primary interface of KFP is the Python SDK, which allows you to define pipelines and reusable components with Python. KFP also provides a Web UI for managing and tracking experiments, pipeline definitions, and pipeline runs. Finally, KFP provides a REST API that allows programmatic access to the platform.

Kubeflow Notebooks

Kubeflow Notebooks lets you run web-based development environments inside a Kubernetes cluster.

PurposeHosting Developer Environments
MaintainerKubeflow Project
DocumentationDocumentation
Source Codekubeflow/kubeflow
deployKF Configskubeflow_tools.notebooks
Since deployKF0.1.0

Kubeflow Notebooks can run any web-based tool, but comes with pre-built images for JupyterLab, RStudio, and Visual Studio Code.

Running development environments inside a Kubernetes cluster has several advantages:

  • Remote Resources: Users can work directly on the cluster, rather than locally on their workstations.
  • Standard Environments: Cluster admins can provide standard environment images for their organization, with required and approved packages pre-installed.
  • Sharing & Access Control: Access is managed via role-based-access-control (RBAC), enabling easier notebook sharing and collaboration across the organization.

Katib

Katib is an Automated Machine Learning (AutoML) platform for Kubernetes.

PurposeAutomated Machine Learning
MaintainerKubeflow Project
DocumentationDocumentation
Source Codekubeflow/katib
deployKF Configskubeflow_tools.katib
Since deployKF0.1.0

The key features of Katib are:

Kubeflow Training Operator

Kubeflow Training Operator helps you run machine learning training jobs on Kubernetes.

PurposeManaging Training Jobs
MaintainerKubeflow Project
DocumentationDocumentation
Source Codekubeflow/training-operator
deployKF Configskubeflow_tools.training_operator
Since deployKF0.1.0

The core function of the Kubeflow Training Operator is to provide Kubernetes Custom Resources (CRDs) that define and monitor training jobs on Kubernetes.

Many popular ML frameworks have been integrated with the Training Operator, including:

Kubeflow Volumes

Kubeflow Volumes is a web-based UI for creating and managing Kubernetes Persistent Volumes.

PurposeManaging Kubernetes Volumes
MaintainerKubeflow Project
DocumentationN/A
Source Codekubeflow/kubeflow
deployKF Configskubeflow_tools.volumes
Since deployKF0.1.0

Kubeflow TensorBoards

Kubeflow TensorBoards is a web-based UI for creating and managing TensorBoard instances on Kubernetes.

PurposeManaging TensorBoards
MaintainerKubeflow Project
DocumentationN/A
Source Codekubeflow/kubeflow
deployKF Configskubeflow_tools.tensorboards
Since deployKF0.1.0

Last update: 2024-03-16
Created: 2023-04-27